Developing integrated process planning and scheduling with dynamic features for multi-objective based on Cooperative game theory

Document Type : Research Paper

Authors

Department of Industrial Engineering, Payame Noor University, Tehran, Iran.

Abstract

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, these two, were carried out in separate and sequential way with a single criterion optimization and regard to some hypothesizes. In real- world these hypothesizes such as resources and machines permanent availability and process planning inflexibility make the solution will become infeasible. In this paper to improve efficiency and adapt more to the real- world production, with four criteria, alternative operation sequences and dynamic feature such as machine breakdown and new order arrival used to optimize integrated process planning and scheduling (IPPS) problem. In solving problem process, cooperative game theory based on compromise method has been developed and a meta heuristic hybrid algorithm (GA ,TS) are used. The approach has been tested and the result show that the developed approach is a proper method to solve a multi objective IPPS with supposed constraints.

Keywords

Main Subjects

References

[1]      Kumar, M., Rajotia, S., (2003). “Integration of scheduling with computer aided process planning” Journal of Materials Processing Technolo, 138: 297-300.
[2]      Khoshnevis, B., and Chen, Q., (1990). “Integration of process planning and scheduling functions”, Journal of Intelligent Manufacturing, 1: 165-176.
[3]      Detand, J., Kruth, J.P., Kempenaers, J., (1992). “A computer aided process planning system that increases the exibility of manufacturing”, IPDES (Espirit project 2590) Workshop.
[4]      Chryssolouris, G., Chan, S., Cobb, W., (1984). “Decision making on the factory floor:an integrated approach to process planning and scheduling”, Robotics and Computer-Integrated Manufacturing, 1(3-4): 9-315.
[5]      Chryssolouris, G., Chan, S., (1985). “An integrated approach to process planning and scheduling”, Annals of the CIRP3, 4(1): 7-413.
[6]      Zhang, H.C., Merchant, M.E., (1993). “IPPM – A prototype to integrate process planning and job shop scheduling function”, CIRP Annals - Manufacturing Technology, 42(1): 513-518.
[7]      Kempenaers, J., Pinte, J., Detand, J., (1996). “A collaborative process planning and scheduling system”, Advances in Engineering Software, 25: 3-8.
[8]      Li, X., Gao, L., Zhang, C., Shao, X., (2010). “A review on integrated process planning and scheduling”, International Journal Manufacturing Research, 5(2): 161-180.
[9]      Rajkumar, M., Asokan, P., (2010). “A GRASP algorithm for the Integration of Process Planning and Scheduling in a flexible job-shop”, Int. J. Manufacturing Research, 5(2): 230-251.
[10]   Dong, J., Jo, H.H., Parsaei, H.R., (1992). “A feature-based dynamic process planning and scheduling”, Computers Industrial Engineering, 23: 141-144.
[11]   Wang, L., Cai, N., Feng, H. Y., Ma, J., (2010). “ASP: an adaptive setup planning approach for dynamic machine assignments”, IEEE, Transactions on Automation Science and Engineering, 7(1): 2-14.
[12]   Chen, Q., Khoshnevis, B., (1993). “Scheduling withflexible process plans”, Production Planning & Control, 4(4): 333-343.
[13]   Kim, Y., Park, K., Ko, J,. (2003). “A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling”, Computers and Operations Research, 30: 1151-1171.
[14]   Zhang, H., Mallur, S., (1994). “An integrated model of process planning and production scheduling”, International Journal of Computer Integrated Manufacturing, 7(6): 356-364.
[15]   Tosserams, S., Etman, L.F.P., Rooda, J.E., (2008). “Augmented Lagrangian coordination for distributed optimal design in MDO”, Int. J. Numer. Meth. Engng., 73: 1885-1910.
[16]   Carpentier, P., Cohen, G., Culioli, J.C., Renaud, A., (1996). “Stochastic optimization of unit commitment: A new decomposition framework”, IEEE Transactions on Power Systems, 11: 1067-1073.
[17]   Brandimarte, P., (1992). “Neighborhood search-based optimization algorithms for production scheduling Ða survey”, Computer Integrated Manufacturing Systems, 5(2): 167-176.
[18]    بهنامیان، جواد.، دیانت، فریبرز.، (1395). «مقایسه سه روش فراابتکاری برای کمینه‌نمودن زمان چرخه در مسئله زمانبندی جریان کارگاهی مختلط دوره‌ای با در نظر گرفتن اثر یادگیری»، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، 4(8): 105-117.
[19]   Weidong, L., Lihui, W., Xinyu, L., Liang, G., (2011). “Intelligent Optimisation for Integrated Process Planning and Scheduling, L.W. Deb, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing”, London: Springer-Verlag London Limited, 305-329.
[20]   Xinyu, Li., Liang, G., Xinyu, S., Chaoyong, Z., Cuiyu, W., (2010). “Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling”, Computers&Operations Research, 37: 656-667
[21]   Li, W.D., Gao, L., Li, X.Y., Guo, Y., (2008). “Game Theory-based Cooperation of Process Planning and Scheduling”, IEEE, 841.
[22]    فخرزاد، محمدباقر.، علی­نژاد، اسماعیل.، (1392). «برنامه‌ریزی و زمان‌بندی پیشرفته با در نظر گرفتن اثر یادگیری در سیستم‌های ساخت کارگاهی انعطاف‌پذیر»، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، 1(1): 13-24.
[23]   Li, X., Gao, L., Li, W., (2012). “Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling”, Expert Systems with Applications, 39: 288-297.
[24]    Haddadzade, M., Razfar, M.R., Fazel Zarandi, M.H., (2014). “Integration of process planning and job shop scheduling with stochastic processing time”, Int. J. Adv. Manuf. Technol., 71: 241-252.
[25]   Mingrang, Y., Yingjie, Z., Kun, C., Ding, Z., (2015). “Integration of process planning and scheduling using a hybrid GA/PSO algorithm”, Int. J. Adv. Manuf. Technol., 78: 583-592.
[26]    Wenqiang, Z., Mitsuo, G., Jungbok, J., (2014). “Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem”, J. Intell. Manuf., 25: 881-897.
[27]   Yunfei, Ch., Fengqi, Y., John, M., Wassick, A.A., (2015). “Integrated planning and scheduling under production uncertainties: Bi-level model formulation andhybrid solution method”, Computers and Chemical Engineering, 72: 255-272
[28]   Luping, Z., Wong, T.N., (2015). “An object-coding genetic algorithm forint integrated process planning and scheduling”, European Journalof Operational Research 244: 434-444.
[29]   Bensmaine, A., Dahane, M., Benyoucef, L.,  (2014). “A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems”, International Journal of Production Research, 52(12), 3583-3594
[30]   Mohapatra, P., Nayak, A., Kumar, S.K., Tiwari, M.K., (2015). “Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm” International Journal of Production Research, 53: 6: 1712-1735.
[31]   Guofu, L., Xiaoyu, W., Hao, L., Wuyi, M., Guizhong, X., (2017). “An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling”, Int. J. Adv. Manuf. Technol., 91: 3145-3158.
[32]   Hao, X., Xinyu, L., Liang, G., (2016). “A hybrid genetic algorithm with variable neighborhood search for dynamic integrated process planning and scheduling” Computers & Industrial Engineering, 102: 99-112.
[33]   Guo, Y.W., Li, W.D., Mileham, A.R., Owen, G.W., (2009). “Applications of particle swarm and optimization in integrated process planning and scheduling”, Robotics and Computer-Integrated Manufacturing, 280: 8-25.
[34]   Baykasoglu, A., Ozbakir, L., (2009). “A grammatical optimization approach for integrated process planning and scheduling”, Journal of Intelligent Manufacturin, 20: 211-221.
Journal of Industrial Engineering Research in Production Systems
Volume 6, Issue 12
September 2018
Pages 19-31

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